Variables

#Data frames with all variables
#2009
dict09 =  iccs09 %>%
  mutate(time          = 2009) %>%
  mutate(country       = COUNTRY) %>%
  mutate(idcountry     = IDCNTRY) %>%
  mutate(idschool       = IDSCHOOL) %>%
  mutate(idstudent     = IDSTUD) %>%
  mutate(idstudent = (time*1000000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  #Authoritarianism
  mutate(dicta1         = 5-LS2P02A) %>% #Government leaders to make decisions without consulting anybody
  mutate(dicta2         = 5-LS2P02B) %>% #People in government must enforce their authority even
  mutate(dicta3         = 5-LS2P02C) %>% #People in government lose part of their authority
  mutate(dicta4         = 5-LS2P02D) %>% #People whose opinions are different must be considered its enemies
  mutate(dicta5         = 5-LS2P02E) %>% #The most important opinion of a country should be that of the pres
  mutate(dicta6         = 5-LS2P02F) %>% #It is fair that the government does not comply with the law
  mutate(dicta7         = 5-LS2P03A) %>% #It is fair that the government does not comply with the law
  mutate(dicta8         = 5-LS2P03B) %>% #Concentration of power in one person guarantees order
  mutate(dicta9         = 5-LS2P03C) %>% #If the president does not agree withCongress>, he should dissolve 
  mutate(dicta_safety   = 5-LS2P03D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dicta_benefits = 5-LS2P03E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict          = (dicta_safety + dicta_benefits)/2) %>% #MEAN DIC
  #Dummies
  mutate(dicta1_d      = recode(dicta1, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta2_d      = recode(dicta2, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta3_d      = recode(dicta3, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta4_d      = recode(dicta4, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta5_d      = recode(dicta5, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta6_d      = recode(dicta6, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta7_d      = recode(dicta7, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta8_d      = recode(dicta8, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta9_d      = recode(dicta9, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_saf_d  = recode(dicta_safety, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_ben_d  = recode(dicta_benefits, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  #Civic Knowledge
  mutate(pv1civ        = PV1CIV) %>%
  mutate(pv2civ        = PV2CIV) %>%
  mutate(pv3civ        = PV3CIV) %>%
  mutate(pv4civ        = PV4CIV) %>%
  mutate(pv5civ        = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  #Independent Variables
  mutate(s_opdisc        = OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = HOMELIT) %>% #HOME LITERACY
  mutate(s_gender        = SGENDER) %>% #GENDER OF STUDENT
  mutate(s_age           = SAGE) %>%    #AGE STUDENT
  mutate(s_citcon        = CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = ATTCNT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = ATTDIFF) %>% #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = EMPATH) %>%  #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES
  #TRUST
  mutate(s_intrust       = INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(nac_gob       = 5 - IS2P27A) %>% #TRUST INSTITUTIONS-NATIONAL GOVERNMENT  
  mutate(local_gob     = 5 - IS2P27B) %>% #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts        = 5 - IS2P27C) %>% #TRUST INSTITUTIONS-COURTS
  mutate(police        = 5 - IS2P27D) %>% #TRUST INSTITUTIONS-POLICE 
  mutate(pol_parties   = 5 - IS2P27E) %>% #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament    = 5 - IS2P27F) %>% #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media         = 5 - IS2P27G) %>% #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa          = 5 - IS2P27H) %>% #TRUST INSTITUTIONS-FFAA 
  mutate(school        = 5 - IS2P27I) %>% #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations  = 5 - IS2P27J) %>% #TRUST INSTITUTIONS-UNITED NATIONS 
  mutate(people        = 5 - IS2P27K) %>% #TRUST INSTITUTIONS-PEOPLE
  mutate(social          = NA) %>%   #TRUST INSTITUTIONS-SOCIAL MEDIA
  #Dummies
  mutate(nac_gob_d         = recode(nac_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(local_gob_d       = recode(local_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(courts_d          = recode(courts, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(police_d          = recode(police, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(pol_parties_d     = recode(pol_parties, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(parliament_d      = recode(parliament, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(media_d           = recode(media, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(ffaa_d            = recode(ffaa, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(school_d          = recode(school, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(unit_nations_d    = recode(unit_nations, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(people_d          = recode(people, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(social_d          = NA) %>%
  #WEITHINGS5 - 
  mutate(totwgts       = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1       = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s      = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s      = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s      = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s      = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones       = JKZONES) %>%   #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps        = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(411:497)
 
#2016  
dict16 = iccs16 %>%
  mutate(time            = 2016) %>%
  mutate(country         = COUNTRY) %>%
  mutate(idcountry       = IDCNTRY) %>%
  mutate(idschool        = IDSCHOOL) %>%
  mutate(idstudent       = IDSTUD) %>%
  mutate(idstudent       = (time*100000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  #Authoritarianism
  mutate(dicta1         = 5-LS3G01A) %>% #Government leaders to make decisions without consulting anybody
  mutate(dicta2         = 5-LS3G01B) %>% #People in government must enforce their authority even
  mutate(dicta3         = 5-LS3G01C) %>% #People in government lose part of their authority
  mutate(dicta4         = 5-LS3G01D) %>% #People whose opinions are different must be considered its enemies
  mutate(dicta5         = 5-LS3G01E) %>% #The most important opinion of a country should be that of the pres
  mutate(dicta6         = 5-LS3G01F) %>% #It is fair that the government does not comply with the law
  mutate(dicta7         = 5-LS3G02A) %>% #It is fair that the government does not comply with the law
  mutate(dicta8         = 5-LS3G02B) %>% #Concentration of power in one person guarantees order
  mutate(dicta9         = 5-LS3G02C) %>% #If the president does not agree withCongress>, he should dissolve
  mutate(dicta_safety    = 5-LS3G02D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dicta_benefits  = 5-LS3G02E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict           = (dicta_safety + dicta_benefits)/2) %>% 
  #Dummies
  mutate(dicta1_d      = recode(dicta1, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta2_d      = recode(dicta2, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta3_d      = recode(dicta3, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta4_d      = recode(dicta4, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta5_d      = recode(dicta5, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta6_d      = recode(dicta6, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta7_d      = recode(dicta7, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta8_d      = recode(dicta8, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta9_d      = recode(dicta9, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_saf_d  = recode(dicta_safety, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_ben_d  = recode(dicta_benefits, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  #Civic Knowledge
  mutate(pv1civ          = PV1CIV) %>%
  mutate(pv2civ          = PV2CIV) %>%
  mutate(pv3civ          = PV3CIV) %>%
  mutate(pv4civ          = PV4CIV) %>%
  mutate(pv5civ          = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  #Independent Variables
  mutate(s_opdisc        = S_OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = S_HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = S_HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = S_HOMLIT) %>%  #HOME LITERACY
  mutate(s_gender        = S_GENDER) %>%  #GENDER OF STUDENT
  mutate(s_age           = S_AGE) %>%     #AGE STUDENT
  mutate(s_citcon        = S_CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = S_CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = S_CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = S_CNTATT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = S_GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = S_ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = L_ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = L_ATTDIV) %>%  #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = L_AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = L_ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = L_DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = L_EMPCLAS) %>% #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = S_POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES  
  #TRUST
  mutate(s_intrust       = S_INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(nac_gob         = 5 - IS3G26A) %>%   #TRUST INSTITUTIONS-NATIONAL GOVERNMENT 
  mutate(local_gob       = 5 - IS3G26B) %>%   #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts          = 5 - IS3G26C) %>%   #TRUST INSTITUTIONS-COURTS
  mutate(police          = 5 - IS3G26D) %>%   #TRUST INSTITUTIONS-POLICE
  mutate(pol_parties     = 5 - IS3G26E) %>%   #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament      = 5 - IS3G26F) %>%   #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media           = 5 - IS3G26G) %>%   #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa            = 5 - IS3G26I) %>%   #TRUST INSTITUTIONS-FFAA
  mutate(school          = 5 - IS3G26J) %>%   #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations    = 5 - IS3G26K) %>%   #TRUST INSTITUTIONS-UNITED NATIONS
  mutate(people          = 5 - IS3G26L) %>%   #TRUST INSTITUTIONS-PEOPLE
  mutate(social          = 5 - IS3G26H) %>%   #TRUST INSTITUTIONS-SOCIAL MEDIA
  #Dummies
  mutate(nac_gob_d         = recode(nac_gob,     "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(local_gob_d       = recode(local_gob,   "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(courts_d          = recode(courts,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(police_d          = recode(police,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(pol_parties_d     = recode(pol_parties, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(parliament_d      = recode(parliament,  "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(media_d           = recode(media,       "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(ffaa_d            = recode(ffaa,        "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(school_d          = recode(school,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(unit_nations_d    = recode(unit_nations,"1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(people_d          = recode(people,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(social_d          = recode(social,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  #WEITHINGS5 - 
  mutate(totwgts         = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1         = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s        = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s        = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s        = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s        = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones         = JKZONES)  %>%  #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps          = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(519:605)
#Merge data
mergeiccs <- full_join(dict09, dict16)
## Joining, by = c("time", "country", "idcountry", "idschool", "idstudent", "dicta1", "dicta2", "dicta3", "dicta4", "dicta5", "dicta6", "dicta7", "dicta8", "dicta9", "dicta_safety", "dicta_benefits", "dict", "dicta1_d", "dicta2_d", "dicta3_d", "dicta4_d", "dicta5_d", "dicta6_d", "dicta7_d", "dicta8_d", "dicta9_d", "dicta_saf_d", "dicta_ben_d", "pv1civ", "pv2civ", "pv3civ", "pv4civ", "pv5civ", "civic_knowledge", "s_opdisc", "s_hisced", "univ", "s_hisei", "s_homelit", "s_gender", "s_age", "s_citcon", "s_citsoc", "s_citeff", "s_cntatt", "s_geneql", "s_ethrght", "l_attviol", "l_attdiv", "l_autgov", "l_attcorr", "l_dislaw", "l_empclas", "s_poldisc", "s_intrust", "nac_gob", "local_gob", "courts", "police", "pol_parties", "parliament", "media", "ffaa", "school", "unit_nations", "people", "social", "nac_gob_d", "local_gob_d", "courts_d", "police_d", "pol_parties_d", "parliament_d", "media_d", "ffaa_d", "school_d", "unit_nations_d", "people_d", "social_d", "totwgts", "wgtfac1", "wgtadj1s", "wgtfac2s", "wgtadj2s", "wgtadj3s", "jkzones", "jkreps")

Sample Size

time ncountry N man age s
2009 Chile 5173 0.5143801 14.17872 177
2016 Chile 5081 0.4928164 14.17268 178
2009 Colombia 6200 0.5352932 14.37904 196
2016 Colombia 5609 0.5229096 14.59167 150
2009 Dominican Republic 4569 0.5468819 14.85543 145
2016 Dominican Republic 3937 0.5128270 14.18576 141
2009 Guatemala 3998 0.4899699 15.51745 145
2009 Mexico 6565 0.5222493 14.08043 215
2016 Mexico 5526 0.5000000 14.03109 213
2009 Paraguay 3391 0.5213801 14.81850 149
2016 Peru 5166 0.4816105 14.03021 206

Institutional Trust

country time nac_gob_d proportion proportion_low proportion_upp
CHL 2009 0 0.3486123 0.3291055 0.3681191
CHL 2009 1 0.6513877 0.6318809 0.6708945
CHL 2009 NA NA NA NA
CHL 2016 0 0.5041627 0.4847861 0.5235392
CHL 2016 1 0.4958373 0.4764608 0.5152139
CHL 2016 NA NA NA NA
COL 2009 0 0.3797472 0.3552204 0.4042741
COL 2009 1 0.6202528 0.5957259 0.6447796
COL 2009 NA NA NA NA
COL 2016 0 0.4476026 0.4236968 0.4715084
COL 2016 1 0.5523974 0.5284916 0.5763032
COL 2016 NA NA NA NA
DOM 2009 0 0.2612638 0.2365092 0.2860184
DOM 2009 1 0.7387362 0.7139816 0.7634908
DOM 2009 NA NA NA NA
DOM 2016 0 0.2219649 0.1992088 0.2447210
DOM 2016 1 0.7780351 0.7552790 0.8007912
DOM 2016 NA NA NA NA
GTM 2009 0 0.5469156 0.5200227 0.5738086
GTM 2009 1 0.4530844 0.4261914 0.4799773
GTM 2009 NA NA NA NA
MEX 2009 0 0.4158023 0.3954032 0.4362014
MEX 2009 1 0.5841977 0.5637986 0.6045968
MEX 2009 NA NA NA NA
MEX 2016 0 0.4299568 0.4090000 0.4509136
MEX 2016 1 0.5700432 0.5490864 0.5910000
MEX 2016 NA NA NA NA
PER 2016 0 0.5096989 0.4893269 0.5300710
PER 2016 1 0.4903011 0.4699290 0.5106731
PER 2016 NA NA NA NA
PRY 2009 0 0.3409936 0.3156542 0.3663329
PRY 2009 1 0.6590064 0.6336671 0.6843458
PRY 2009 NA NA NA NA
country time police_d proportion proportion_low proportion_upp
CHL 2009 0 0.2896503 0.2717131 0.3075874
CHL 2009 1 0.7103497 0.6924126 0.7282869
CHL 2009 NA NA NA NA
CHL 2016 0 0.3551635 0.3366103 0.3737167
CHL 2016 1 0.6448365 0.6262833 0.6633897
CHL 2016 NA NA NA NA
COL 2009 0 0.4507952 0.4293250 0.4722654
COL 2009 1 0.5492048 0.5277346 0.5706750
COL 2009 NA NA NA NA
COL 2016 0 0.5076088 0.4828832 0.5323344
COL 2016 1 0.4923912 0.4676656 0.5171168
COL 2016 NA NA NA NA
DOM 2009 0 0.4369578 0.4107802 0.4631354
DOM 2009 1 0.5630422 0.5368646 0.5892198
DOM 2009 NA NA NA NA
DOM 2016 0 0.4393891 0.4152856 0.4634927
DOM 2016 1 0.5606109 0.5365073 0.5847144
DOM 2016 NA NA NA NA
GTM 2009 0 0.6664363 0.6423384 0.6905341
GTM 2009 1 0.3335637 0.3094659 0.3576616
GTM 2009 NA NA NA NA
MEX 2009 0 0.5699461 0.5528957 0.5869966
MEX 2009 1 0.4300539 0.4130034 0.4471043
MEX 2009 NA NA NA NA
MEX 2016 0 0.5097115 0.4933779 0.5260450
MEX 2016 1 0.4902885 0.4739550 0.5066221
MEX 2016 NA NA NA NA
PER 2016 0 0.4978979 0.4810746 0.5147213
PER 2016 1 0.5021021 0.4852787 0.5189254
PER 2016 NA NA NA NA
PRY 2009 0 0.5494438 0.5270717 0.5718159
PRY 2009 1 0.4505562 0.4281841 0.4729283
PRY 2009 NA NA NA NA
country time pol_parties_d proportion proportion_low proportion_upp
CHL 2009 0 0.6550144 0.6346769 0.6753518
CHL 2009 1 0.3449856 0.3246482 0.3653231
CHL 2009 NA NA NA NA
CHL 2016 0 0.6745075 0.6587782 0.6902368
CHL 2016 1 0.3254925 0.3097632 0.3412218
CHL 2016 NA NA NA NA
COL 2009 0 0.6506877 0.6295206 0.6718549
COL 2009 1 0.3493123 0.3281451 0.3704794
COL 2009 NA NA NA NA
COL 2016 0 0.7228073 0.7029239 0.7426906
COL 2016 1 0.2771927 0.2573094 0.2970761
COL 2016 NA NA NA NA
DOM 2009 0 0.4887925 0.4644688 0.5131161
DOM 2009 1 0.5112075 0.4868839 0.5355312
DOM 2009 NA NA NA NA
DOM 2016 0 0.5024324 0.4798019 0.5250629
DOM 2016 1 0.4975676 0.4749371 0.5201981
DOM 2016 NA NA NA NA
GTM 2009 0 0.7368829 0.7169853 0.7567805
GTM 2009 1 0.2631171 0.2432195 0.2830147
GTM 2009 NA NA NA NA
MEX 2009 0 0.6535926 0.6329986 0.6741866
MEX 2009 1 0.3464074 0.3258134 0.3670014
MEX 2009 NA NA NA NA
MEX 2016 0 0.6261329 0.6059780 0.6462878
MEX 2016 1 0.3738671 0.3537122 0.3940220
MEX 2016 NA NA NA NA
PER 2016 0 0.6660155 0.6471337 0.6848973
PER 2016 1 0.3339845 0.3151027 0.3528663
PER 2016 NA NA NA NA
PRY 2009 0 0.6755932 0.6567009 0.6944855
PRY 2009 1 0.3244068 0.3055145 0.3432991
PRY 2009 NA NA NA NA
country time people_d proportion proportion_low proportion_upp
CHL 2009 0 0.4825848 0.4652249 0.4999446
CHL 2009 1 0.5174152 0.5000554 0.5347751
CHL 2009 NA NA NA NA
CHL 2016 0 0.5222508 0.5053029 0.5391987
CHL 2016 1 0.4777492 0.4608013 0.4946971
CHL 2016 NA NA NA NA
COL 2009 0 0.5116344 0.4933059 0.5299628
COL 2009 1 0.4883656 0.4700372 0.5066941
COL 2009 NA NA NA NA
COL 2016 0 0.5651447 0.5430788 0.5872106
COL 2016 1 0.4348553 0.4127894 0.4569212
COL 2016 NA NA NA NA
DOM 2009 0 0.3897426 0.3636206 0.4158645
DOM 2009 1 0.6102574 0.5841355 0.6363794
DOM 2009 NA NA NA NA
DOM 2016 0 0.3801493 0.3586919 0.4016067
DOM 2016 1 0.6198507 0.5983933 0.6413081
DOM 2016 NA NA NA NA
GTM 2009 0 0.5258643 0.5041887 0.5475399
GTM 2009 1 0.4741357 0.4524601 0.4958113
GTM 2009 NA NA NA NA
MEX 2009 0 0.5341219 0.5191580 0.5490859
MEX 2009 1 0.4658781 0.4509141 0.4808420
MEX 2009 NA NA NA NA
MEX 2016 0 0.4829065 0.4638750 0.5019381
MEX 2016 1 0.5170935 0.4980619 0.5361250
MEX 2016 NA NA NA NA
PER 2016 0 0.5284377 0.5113651 0.5455104
PER 2016 1 0.4715623 0.4544896 0.4886349
PER 2016 NA NA NA NA
PRY 2009 0 0.4276067 0.4082165 0.4469968
PRY 2009 1 0.5723933 0.5530032 0.5917835
PRY 2009 NA NA NA NA

Support authoritarianism

country time dicta_ben_d proportion proportion_low proportion_upp
CHL 2009 0 0.3599497 0.3400556 0.3798438
CHL 2009 1 0.6400503 0.6201562 0.6599444
CHL 2009 NA NA NA NA
CHL 2016 0 0.4844084 0.4650626 0.5037541
CHL 2016 1 0.5155916 0.4962459 0.5349374
CHL 2016 NA NA NA NA
COL 2009 0 0.3004007 0.2878003 0.3130012
COL 2009 1 0.6995993 0.6869988 0.7121997
COL 2009 NA NA NA NA
COL 2016 0 0.3248039 0.3035032 0.3461047
COL 2016 1 0.6751961 0.6538953 0.6964968
COL 2016 NA NA NA NA
DOM 2009 0 0.3398834 0.3180054 0.3617615
DOM 2009 1 0.6601166 0.6382385 0.6819946
DOM 2009 NA NA NA NA
DOM 2016 0 0.2997769 0.2781407 0.3214131
DOM 2016 1 0.7002231 0.6785869 0.7218593
DOM 2016 NA NA NA NA
GTM 2009 0 0.2540651 0.2357930 0.2723373
GTM 2009 1 0.7459349 0.7276627 0.7642070
GTM 2009 NA NA NA NA
MEX 2009 0 0.3382672 0.3235268 0.3530077
MEX 2009 1 0.6617328 0.6469923 0.6764732
MEX 2009 NA NA NA NA
MEX 2016 0 0.3357496 0.3159656 0.3555335
MEX 2016 1 0.6642504 0.6444665 0.6840344
MEX 2016 NA NA NA NA
PER 2016 0 0.2820944 0.2654873 0.2987014
PER 2016 1 0.7179056 0.7012986 0.7345127
PER 2016 NA NA NA NA
PRY 2009 0 0.3533853 0.3347247 0.3720458
PRY 2009 1 0.6466147 0.6279542 0.6652753
PRY 2009 NA NA NA NA
country time dicta_saf_d proportion proportion_low proportion_upp
CHL 2009 0 0.3473609 0.3264615 0.3682604
CHL 2009 1 0.6526391 0.6317396 0.6735385
CHL 2009 NA NA NA NA
CHL 2016 0 0.4344875 0.4130932 0.4558818
CHL 2016 1 0.5655125 0.5441182 0.5869068
CHL 2016 NA NA NA NA
COL 2009 0 0.2604319 0.2465840 0.2742798
COL 2009 1 0.7395681 0.7257202 0.7534160
COL 2009 NA NA NA NA
COL 2016 0 0.2731401 0.2575872 0.2886929
COL 2016 1 0.7268599 0.7113071 0.7424128
COL 2016 NA NA NA NA
DOM 2009 0 0.2953395 0.2751511 0.3155278
DOM 2009 1 0.7046605 0.6844722 0.7248489
DOM 2009 NA NA NA NA
DOM 2016 0 0.2653299 0.2474511 0.2832088
DOM 2016 1 0.7346701 0.7167912 0.7525489
DOM 2016 NA NA NA NA
GTM 2009 0 0.2154922 0.1987022 0.2322822
GTM 2009 1 0.7845078 0.7677178 0.8012978
GTM 2009 NA NA NA NA
MEX 2009 0 0.3148958 0.2981412 0.3316504
MEX 2009 1 0.6851042 0.6683496 0.7018588
MEX 2009 NA NA NA NA
MEX 2016 0 0.3291197 0.3098661 0.3483732
MEX 2016 1 0.6708803 0.6516268 0.6901339
MEX 2016 NA NA NA NA
PER 2016 0 0.2281320 0.2112895 0.2449745
PER 2016 1 0.7718680 0.7550255 0.7887105
PER 2016 NA NA NA NA
PRY 2009 0 0.3052552 0.2857836 0.3247268
PRY 2009 1 0.6947448 0.6752732 0.7142164
PRY 2009 NA NA NA NA

Latin American 2009-2016

Support Authoritarianism

## quartz_off_screen 
##                 2
## quartz_off_screen 
##                 2

Institutuional Trust

## quartz_off_screen 
##                 2
## quartz_off_screen 
##                 2
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##                 2
## quartz_off_screen 
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## quartz_off_screen 
##                 2

Interaction

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 75.87*** -178.68*** -180.92***
(0.32) (23.00) (23.01)
s_intrust 0.17*** 0.17*** 0.26***
(0.00) (0.00) (0.02)
civic_knowledge -0.08*** -0.08*** -0.07***
(0.00) (0.00) (0.00)
s_hisced 0.03 0.03
(0.03) (0.03)
s_homelit -0.19*** -0.19***
(0.04) (0.04)
s_gender -1.16*** -1.16***
(0.08) (0.08)
s_poldisc -0.02*** -0.02***
(0.00) (0.00)
time 0.13*** 0.13***
(0.01) (0.01)
s_intrust:civic_knowledge -0.00***
(0.00)
R2 0.39 0.39 0.39
Adj. R2 0.39 0.39 0.39
Num. obs. 51794 50281 50281
RMSE 97.78 97.69 97.67
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 63.10*** 0.07
(0.26) (27.35)
civic_knowledge -0.03*** -0.03***
(0.00) (0.00)
s_hisced 0.03
(0.03)
s_homelit -0.31***
(0.04)
s_gender -1.04***
(0.09)
s_poldisc 0.12***
(0.00)
time 0.03*
(0.01)
R2 0.05 0.07
Adj. R2 0.05 0.07
Num. obs. 51954 50420
RMSE 117.01 116.37
p < 0.001, p < 0.01, p < 0.05

quartz_off_screen 2

OLS by chile

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 72.44*** 793.05*** 792.12***
(0.71) (56.41) (56.36)
s_intrust 0.20*** 0.18*** 0.37***
(0.01) (0.01) (0.05)
civic_knowledge -0.07*** -0.07*** -0.05***
(0.00) (0.00) (0.00)
s_hisced -0.02 -0.02
(0.09) (0.09)
s_homelit -0.14 -0.14
(0.09) (0.09)
s_gender -0.67*** -0.67***
(0.18) (0.18)
s_poldisc 0.01 0.01
(0.01) (0.01)
time -0.36*** -0.36***
(0.03) (0.03)
s_intrust:civic_knowledge -0.00***
(0.00)
R2 0.36 0.38 0.38
Adj. R2 0.36 0.38 0.38
Num. obs. 10071 9855 9855
RMSE 62.81 62.23 62.18
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 56.49*** 949.93***
(0.61) (64.44)
civic_knowledge -0.02*** -0.02***
(0.00) (0.00)
s_hisced -0.08
(0.11)
s_homelit -0.21*
(0.10)
s_gender -0.86***
(0.21)
s_poldisc 0.13***
(0.01)
time -0.45***
(0.03)
R2 0.02 0.05
Adj. R2 0.02 0.05
Num. obs. 10089 9873
RMSE 73.29 71.94
p < 0.001, p < 0.01, p < 0.05

OLS by Colombia

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 77.16*** -268.35*** -268.65***
(0.66) (44.89) (44.89)
s_intrust 0.10*** 0.11*** 0.16***
(0.01) (0.01) (0.05)
civic_knowledge -0.07*** -0.07*** -0.07***
(0.00) (0.00) (0.00)
s_hisced 0.07 0.07
(0.05) (0.05)
s_homelit -0.13 -0.13
(0.07) (0.07)
s_gender -1.28*** -1.28***
(0.15) (0.15)
s_poldisc -0.03*** -0.03***
(0.01) (0.01)
time 0.17*** 0.17***
(0.02) (0.02)
s_intrust:civic_knowledge -0.00
(0.00)
R2 0.35 0.36 0.36
Adj. R2 0.35 0.36 0.36
Num. obs. 11233 11038 11038
RMSE 83.19 82.73 82.72
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 62.34*** 258.87***
(0.59) (55.83)
civic_knowledge -0.03*** -0.03***
(0.00) (0.00)
s_hisced 0.24***
(0.06)
s_homelit -0.24**
(0.09)
s_gender -1.80***
(0.18)
s_poldisc 0.16***
(0.01)
time -0.10***
(0.03)
R2 0.04 0.08
Adj. R2 0.04 0.08
Num. obs. 11262 11064
RMSE 105.09 103.12
p < 0.001, p < 0.01, p < 0.05

OLS by Dominican Republic

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 74.97*** -169.07** -169.51**
(0.85) (60.43) (60.43)
s_intrust 0.13*** 0.12*** 0.17**
(0.01) (0.01) (0.05)
civic_knowledge -0.07*** -0.07*** -0.07***
(0.00) (0.00) (0.01)
s_hisced 0.03 0.03
(0.08) (0.08)
s_homelit -0.44*** -0.44***
(0.10) (0.10)
s_gender -0.85*** -0.85***
(0.21) (0.21)
s_poldisc -0.02 -0.02
(0.01) (0.01)
time 0.12*** 0.12***
(0.03) (0.03)
s_intrust:civic_knowledge -0.00
(0.00)
R2 0.31 0.32 0.32
Adj. R2 0.31 0.32 0.32
Num. obs. 7117 6655 6655
RMSE 48.31 47.95 47.95
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 71.67*** -228.12**
(0.75) (79.93)
civic_knowledge -0.04*** -0.04***
(0.00) (0.00)
s_hisced -0.12
(0.10)
s_homelit -0.17
(0.13)
s_gender -1.58***
(0.27)
s_poldisc 0.09***
(0.01)
time 0.15***
(0.04)
R2 0.07 0.09
Adj. R2 0.07 0.09
Num. obs. 7160 6685
RMSE 64.17 63.62
p < 0.001, p < 0.01, p < 0.05

OLS by Mexico

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 77.58*** -267.31*** -276.62***
(0.71) (50.95) (50.97)
s_intrust 0.20*** 0.19*** 0.39***
(0.01) (0.01) (0.05)
civic_knowledge -0.08*** -0.08*** -0.06***
(0.00) (0.00) (0.01)
s_hisced 0.03 0.03
(0.06) (0.06)
s_homelit -0.21** -0.22**
(0.08) (0.08)
s_gender -1.05*** -1.05***
(0.17) (0.17)
s_poldisc -0.02* -0.02*
(0.01) (0.01)
time 0.17*** 0.17***
(0.03) (0.03)
s_intrust:civic_knowledge -0.00***
(0.00)
R2 0.40 0.41 0.41
Adj. R2 0.40 0.41 0.41
Num. obs. 11583 11379 11379
RMSE 166.24 165.07 164.96
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 63.18*** -272.13***
(0.57) (58.97)
civic_knowledge -0.03*** -0.03***
(0.00) (0.00)
s_hisced -0.04
(0.07)
s_homelit -0.34***
(0.09)
s_gender -0.74***
(0.20)
s_poldisc 0.10***
(0.01)
time 0.16***
(0.03)
R2 0.05 0.06
Adj. R2 0.05 0.06
Num. obs. 11634 11426
RMSE 193.85 191.71
p < 0.001, p < 0.01, p < 0.05

OLS by Guatemala 2009

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 74.49*** 75.83*** 89.85***
(1.11) (1.32) (3.62)
s_intrust 0.11*** 0.11*** -0.19*
(0.01) (0.01) (0.07)
civic_knowledge -0.07*** -0.07*** -0.10***
(0.00) (0.00) (0.01)
s_hisced 0.23** 0.26***
(0.08) (0.08)
s_homelit -0.13 -0.13
(0.11) (0.11)
s_gender -1.34*** -1.36***
(0.24) (0.24)
s_poldisc -0.00 0.00
(0.01) (0.01)
s_intrust:civic_knowledge 0.00***
(0.00)
R2 0.35 0.36 0.36
Adj. R2 0.35 0.36 0.36
Num. obs. 3798 3684 3684
RMSE 41.28 41.24 41.15
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 65.52*** 58.11***
(0.93) (1.35)
civic_knowledge -0.04*** -0.04***
(0.00) (0.00)
s_hisced -0.17
(0.10)
s_homelit -0.26
(0.14)
s_gender -0.74*
(0.30)
s_poldisc 0.12***
(0.02)
R2 0.10 0.12
Adj. R2 0.10 0.12
Num. obs. 3804 3690
RMSE 52.42 51.66
p < 0.001, p < 0.01, p < 0.05
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

OLS by Paraguay 2009

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 69.95*** 71.15*** 71.25***
(0.84) (1.02) (2.71)
s_intrust 0.10*** 0.09*** 0.09
(0.01) (0.01) (0.05)
civic_knowledge -0.05*** -0.06*** -0.06***
(0.00) (0.00) (0.01)
s_hisced 0.35*** 0.35***
(0.08) (0.08)
s_homelit -0.04 -0.04
(0.10) (0.10)
s_gender -1.67*** -1.67***
(0.20) (0.20)
s_poldisc -0.00 -0.00
(0.01) (0.01)
s_intrust:civic_knowledge 0.00
(0.00)
R2 0.35 0.36 0.36
Adj. R2 0.35 0.36 0.36
Num. obs. 5034 4914 4914
RMSE 69.71 69.44 69.45
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 63.12*** 55.90***
(0.68) (1.08)
civic_knowledge -0.03*** -0.03***
(0.00) (0.00)
s_hisced 0.15
(0.11)
s_homelit 0.19
(0.14)
s_gender -1.75***
(0.27)
s_poldisc 0.14***
(0.01)
R2 0.09 0.12
Adj. R2 0.09 0.11
Num. obs. 5037 4916
RMSE 92.75 91.50
p < 0.001, p < 0.01, p < 0.05
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

OLS by Peru 2016

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
Model 1 Model 2 Model 3
(Intercept) 70.30*** 71.54*** 76.89***
(1.19) (1.40) (4.16)
s_intrust 0.13*** 0.10*** -0.01
(0.01) (0.02) (0.08)
civic_knowledge -0.06*** -0.06*** -0.07***
(0.00) (0.00) (0.01)
s_hisced 0.15 0.15
(0.10) (0.10)
s_homelit -0.02 -0.01
(0.14) (0.14)
s_gender -1.40*** -1.41***
(0.28) (0.28)
s_poldisc 0.01 0.01
(0.01) (0.01)
s_intrust:civic_knowledge 0.00
(0.00)
R2 0.36 0.36 0.36
Adj. R2 0.35 0.36 0.36
Num. obs. 2958 2756 2756
RMSE 36.43 36.54 36.54
p < 0.001, p < 0.01, p < 0.05
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
Model 1 Model 2
(Intercept) 63.22*** 59.21***
(0.92) (1.28)
civic_knowledge -0.03*** -0.03***
(0.00) (0.00)
s_hisced 0.05
(0.12)
s_homelit 0.23
(0.17)
s_gender -2.32***
(0.34)
s_poldisc 0.10***
(0.02)
R2 0.07 0.10
Adj. R2 0.07 0.10
Num. obs. 2968 2766
RMSE 45.62 44.53
p < 0.001, p < 0.01, p < 0.05
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading